{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2025-03-02T16:01:32.953510Z",
     "start_time": "2025-03-02T16:01:32.894757Z"
    }
   },
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "us_file_path = \"./youtube_video_data/US_video_data_numbers.csv\"\n",
    "uk_file_path = \"./youtube_video_data/GB_video_data_numbers.csv\"\n",
    "\"\"\"\n",
    "np.loadtxt() 是 NumPy 库中一个非常实用的函数，主要用于从文本文件中读取数据并将其转换为 NumPy 数组。\n",
    "各个参数太多，具体用法问豆包\n",
    "\"\"\"\n",
    "t_us = np.loadtxt(us_file_path,delimiter=\",\",dtype=\"int\")\n",
    "print(t_us.shape)\n",
    "t_us_comments = t_us[:,3]  # 取第四列数据，即评论数\n",
    "t_us_comments =t_us_comments[t_us_comments<5000]\n",
    "print(t_us_comments.max(),t_us_comments.min())\n",
    "distance = 100  #\n",
    "bin_num = (t_us_comments.max()-t_us_comments.min())//distance\n",
    "plt.figure(figsize=(20,8),dpi=80)  # 设置画布大小\n",
    "plt.hist(t_us_comments,bins=bin_num)\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1688, 4)\n",
      "4995 0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1600x640 with 1 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 13
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-02T16:20:35.063257Z",
     "start_time": "2025-03-02T16:20:35.012292Z"
    }
   },
   "cell_type": "code",
   "source": [
    "t_uk = np.loadtxt(uk_file_path,delimiter=\",\",dtype=\"int\")\n",
    "print(t_uk.shape)\n",
    "t_uk_comments = t_uk[:,3]  # 取第四列数据，即评论数\n",
    "print(t_uk_comments.max(),t_uk_comments.min())\n",
    "x = range(1600)\n",
    "distance = 10000\n",
    "bin_num = (t_uk_comments.max()-t_uk_comments.min())//distance\n",
    "plt.figure(figsize=(20,8),dpi=80)\n",
    "plt.scatter(x,t_uk_comments,s=1)\n",
    "plt.show()"
   ],
   "id": "b6f6f0134eb6cd79",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1600, 4)\n",
      "582505 0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1600x640 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 20
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
